Source code for bob.pad.voice.extractor.lbps

#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Pavel Korshunov <>
# Tue 17 May 15:43:22 CEST 2016

import numpy
import bob.ip.base

import bob.sp

from .ratios import Ratios

import math

import logging

logger = logging.getLogger("bob.pad.voice")

class LBPs(Ratios):
    Extractor that computes histograms of LBP features from a textogram, which, in turn, is computed by a cepstral
    or spectral extractor passed as an argument.

    def __init__(self,
                 features_processor,  # another extractor that provides features for LBP computation
        self.lbp_neighbors = lbp_neighbors
        self.lbp_to_average = lbp_to_average
        self.lbp_elbp_type = lbp_elbp_type
        self.lbp_uniform = lbp_uniform
        self.lbp_circular = lbp_circular
        self.lbp_radius = lbp_radius
        self.band_ratios = band_ratios

[docs] def compute_lbps(self, data): # find the size of each textogram (a stip of features, for which we compute LBP) textogram_width = int(math.floor(self.features_processor.n_filters / self.n_bands)) lbpimages = [] lbp = bob.ip.base.LBP(neighbors=self.lbp_neighbors, circular=self.lbp_circular, radius=self.lbp_radius, to_average=self.lbp_to_average, uniform=self.lbp_uniform, elbp_type=self.lbp_elbp_type) for i in range(0, self.n_bands): textogram = data[:, i * textogram_width:(i + 1) * textogram_width] if textogram.max(): textogram *= 255.0 / textogram.max() textogram = numpy.asarray(textogram, dtype=numpy.uint8) lbpimage = numpy.ndarray(lbp.lbp_shape(textogram), 'uint16') # allocating the image with lbp codes lbp(textogram, lbpimage) # calculating the lbp image lbpimages.append(lbpimage) return lbp, lbpimages
[docs] def get_features(self, input_data, annotations): # spectrogram = SpectrogramExtended.__call__(input_data, annotations) if self.features_processor is not None: return self.features_processor(input_data, annotations)"- Extraction: spectrogram is empty, returning zero vector...") hist_size = 1 if self.lbp_neighbors == 16: hist_size = 16 # we keep only 16 fft coefficients elif self.lbp_neighbors == 8: hist_size = 256 # the size of histograms when LBP is computed on 8 neighbors return numpy.array([numpy.zeros(hist_size * self.n_bands + self.n_bands)])
def __call__(self, input_data, annotations=None): """Computed LBP histograms from cepstral or spectrogram features""" # spectrogram = SpectrogramExtended.__call__(input_data, annotations) spectrogram = self.get_features(input_data, annotations) lbp, lbpimages = self.compute_lbps(spectrogram) ratios = [] if self.band_ratios: ratios = self.compute_ratios(spectrogram) # concatenate histograms together in one numpy array features = [] for i in range(0, self.n_bands): features.append(lbpimages[i].flatten()) # just put into the larger list features = numpy.append(ratios, features)"- Extraction: size of the LBP feature vector of size %s", str(features.shape)) return numpy.asarray(features, dtype=numpy.float64) from .spectrogram_extended import SpectrogramExtended extractor = LBPs(features_processor=SpectrogramExtended())